Robust modulation of integrate-and-fire models (Van Pottelbergh et al 2018)

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Accession:235138
"By controlling the state of neuronal populations, neuromodulators ultimately affect behavior. A key neuromodulation mechanism is the alteration of neuronal excitability via the modulation of ion channel expression. This type of neuromodulation is normally studied with conductance-based models, but those models are computationally challenging for large-scale network simulations needed in population studies. This article studies the modulation properties of the multiquadratic integrate-and-fire model, a generalization of the classical quadratic integrate-and-fire model. The model is shown to combine the computational economy of integrate-and-fire modeling and the physiological interpretability of conductance-based modeling. It is therefore a good candidate for affordable computational studies of neuromodulation in large networks."
Reference:
1 . Van Pottelbergh T, Drion G, Sepulchre R (2018) Robust Modulation of Integrate-and-Fire Models. Neural Comput 30:987-1011 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Neuron or other electrically excitable cell;
Brain Region(s)/Organism:
Cell Type(s): Abstract Izhikevich neuron; Abstract integrate-and-fire adaptive exponential (AdEx) neuron; Abstract integrate-and-fire neuron;
Channel(s):
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment: Brian; Brian 2;
Model Concept(s): Bifurcation; Bursting; Action Potential Initiation; Delay; Multiscale; Neuromodulation;
Implementer(s): Van Pottelbergh, Tomas [tmjv2 at cam.ac.uk];
from brian2 import *

# Izhikevich
Izhikevich_eqs = '''
dv/dt = (0.04*v**2 + 5*v + 140 - u + I)/ms : 1
du/dt = a*(b*v - u)/ms : 1
'''

Izhikevich_threshold = 'v > v_th'
Izhikevich_reset = '''
v = c
u = u + d
'''

# MQIF
MQIF_eqs = '''
dv/dt = (g_f*(v-v_f0)**2 - g_s*(v_s-v_s0)**2 - g_u*(v_u-v_u0)**2 + I)/C : 1
dv_s/dt = (v - v_s)/tau_s : 1
dv_u/dt = (v - v_u)/tau_u : 1
'''

MQIF_threshold = 'v > v_th'
MQIF_reset = '''
v = v_f0
v_s = v_sr
v_u = v_u + dv_u
'''

# MQIF with ultra-ultraslow timescale
MQIF2_eqs = '''
dv/dt = (g_f*(v-v_f0)**2 - g_s*(v_s-v_s0)**2 - g_u*(v_u-v_u0)**2 - g_uu*(v_uu-v_uu0)**2 + I)/C : 1
dv_s/dt = (v - v_s)/tau_s : 1
dv_u/dt = (v - v_u)/tau_u : 1
dv_uu/dt = (v - v_uu)/tau_uu : 1
'''

MQIF2_threshold = 'v > v_th'
MQIF2_reset = '''
v = v_f0
v_s = v_sr
v_u = v_u + dv_u
v_uu = v_uu + dv_uu
'''

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